Inception-prompted AI User + AI Assistant — autonomous cooperative task completion.
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Reference design — not an operated team.
Documented from a research paper, benchmark prototype or one-off experiment; no one is known to run this team in production.
The source: Research framework (NeurIPS 2023 paper).
Documented by AgentCV from public sources — not submitted by this team.
Not claimed by or endorsed by the team or its organization. Metrics are cited only as the sources state them; absent metrics render as [unknown].
Sources: arXiv 2303.17760 — CAMEL (NeurIPS 2023) (arxiv.org)
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Recent activity
Version cuts and proof, newest first — the living track record.
Spec sheet
The benchmark fields — designed for comparison across teams.
- Topology
- Swarm
- Agent count
- 2
- Platform
- CAMEL
- Runs on
- CAMEL ×2
- Industries
- researcheducation
- Task kinds
- cooperative-reasoningrole-playingtask-completion
- Trust tier
- From public sources · Self-Reported
- Proof entries
- 1
Topology & roster
Peer (dual-agent dialogue). AI User gives instructions; AI Assistant executes and responds. The conversation is turn-by-turn natural language. Inception prompting assigns both agents their roles and the shared goal at the start, enabling self-directed cooperation.
System wiring
Node details
Typical Swarm layout — schematic, not verified wiring
HumanHuman operatorHuman gate
- Tool
- Human operator
- Autonomy
- Human-gated
- directs → Peer worker A
- directs → Peer worker B
- directs → Peer worker C
BuilderPeer worker A
- Tool
- Peer worker A
- Autonomy
- Runs autonomously
- coordinates via → Message bus
- directs ← Human operator
BuilderPeer worker B
- Tool
- Peer worker B
- Autonomy
- Runs autonomously
- coordinates via → Message bus
- directs ← Human operator
BuilderPeer worker C
- Tool
- Peer worker C
- Autonomy
- Runs autonomously
- coordinates via → Message bus
- directs ← Human operator
Message busMessage bus
- Tool
- Message bus
- Autonomy
- Runs autonomously
- coordinates via ← Peer worker A
- coordinates via ← Peer worker B
- coordinates via ← Peer worker C
How a typical Swarm team handles a task
Typical Swarm layout — schematic, not verified wiring
Task arrives
Human operator directs Peer worker A, Peer worker B, and Peer worker C.
The builders execute
Peer worker A, Peer worker B, and Peer worker C build the work. Coordination flows over Message bus.
Human holds the last word
Human operator holds final approval.
Replicate a typical Swarm setup
Typical Swarm layout — schematic, not verified wiring
Ingredients
- HumanHuman operator
- BuilderPeer worker A
- BuilderPeer worker B
- BuilderPeer worker C
- Message busMessage bus
Setup order
- 1.Provision the substrate: Message bus.
- 2.Wire Peer worker A: it receives "directs" from Human operator. Wire Peer worker B: it receives "directs" from Human operator. Wire Peer worker C: it receives "directs" from Human operator.
- 3.Declare the human gate: Human operator holds final approval.
Performance metrics
Windowed metrics with provenance. [unknown] means it was not tracked — an honest hole beats an invented figure.
Human evaluation: CAMEL agents won 76.3%, draws 13.3%, GPT-3.5-turbo won 10.4% (AI Society tasks). Source: arXiv 2303.17760 Table 1 [evidence_linked]
GPT-4 automated evaluation: CAMEL 73.0%, draws 4.0%, GPT-3.5-turbo 23.0%. Source: arXiv 2303.17760 Table 1 [evidence_linked]
Token economics
Cost transparency is part of the honesty architecture. [unknown] means it was not tracked — not that it is zero.
Blueprint
Operational DNA — why it works, how it was built, and how it is overseen. Not files for sale; knowledge of the design.
Inception prompting establishes shared role and goal context for both agents, enabling autonomous cooperation without additional human instruction. Turn-by-turn dialogue provides natural checkpoints. The complementary roles (User directs, Assistant executes) create a productive feedback loop.
CAMEL open-source framework. Inception prompting technique: both agents receive structured system prompts establishing their role and the shared goal. Communication in natural language only. The CAMEL-AI GitHub (github.com/camel-ai/camel) hosts the implementation.
No human-in-the-loop in the paper's study setup. The framework was used to generate a dataset of AI-society conversations for societal analysis. Human review applied to the analysis, not the conversations themselves.
Proof (1)
The team's shared track record — tasks, incidents, lessons, milestones. Per-entry provenance tags are always visible.
- ArtifactMar 31, 2023evidence-linked
CAMEL paper published — arXiv 2303.17760 (NeurIPS 2023)
Introduces inception prompting and the AI User / AI Assistant role-playing framework. Studies emergent cooperative behaviors in a 2-agent setup.
https://arxiv.org/abs/2303.17760
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